Abstract
This work describes an optimum utilization of hybrid photovoltaic (PV)—wind energy for residential buildings on its occurrence with a newly proposed autonomous fuzzy controller (AuFuCo). In this regard, a virtual model of a vertical axis wind turbine (VAWT) and PV system (each rated at 2 kW) are constructed in a MATLAB Simulink environment. An autonomous fuzzy inference system is applied to model primary units of the controller such as load forecasting (LF), grid power selection (GPS) switch, renewable energy management system (REMS), and fuzzy load switch (FLS). The residential load consumption pattern (4 kW of connected load) is allowed to consume energy from the grid and hybrid resources located at the demand side and classified as base, priority, short-term, and schedulable loads. The simulation results identify that the proposed controller manages the demand side management (DSM) techniques for peak load shifting and valley filling effectively with renewable sources. Also, energy costs and savings for the home environment are evaluated using the proposed controller. Further, the energy conservation technique is studied by increasing renewable conversion efficiency (18% to 23% for PV and 35% to 45% for the VAWT model), which reduces the spending of 0.5% in energy cost and a 1.25% reduction in grid demand for 24-time units/day of the simulation study. Additionally, the proposed controller is adapted for computing energy cost (considering the same load pattern) for future demand, and it is exposed that the PV-wind energy cost reduced to 6.9% but 30.6% increase of coal energy cost due to its rise in the Indian energy market by 2030.
Highlights
Presented a flexible allocation strategy based on a two-stage fuzzy logic controller to address energy management and cost control [18]
The proposed smart fuzzy logic controller (SFLC) executes output through four major fuzzy inference systems. They are: (1) forecasting system (FS), (2) grid power selection (GPS) switch, (3) renewable energy management system (REMS) and (4) fuzzy load switch (FLS). These fuzzy inference systems are connected with the hybrid wind-solar virtual energy resources and perform the control actions as per the fuzzy rules framed with the real-time input variables
This hybrid combination is recommended for domestic applications that allow consumers to load the schedulable demand rate during the off-peak grid period
Summary
It is observed that the penetration of renewable energy4roefs2o9urces shows greater advantages, and the optimum utilization and cost computation are demonstrated extensively using different optimizer tools. The vρir=tuAailr mdeondseitlyis(kags/omft3w)=a1r.e23representation of a physical model designed using its cohuatpraucttedreitsAatii=clsDesHqimu=aiTltaiuorrnbtosinatehnsodwsetehpoetfoaarpreeearaal(t-mitni2mg)epmaraacmheinteer.sVairretueaml 2bekdWdePdVtoanpdroVvAidWeTinmpoudt uanleds are construDc=teDdiaimn etthreoMf AthTeVLAAWBTSi(mmu)link environment for supplying a sanctioned 4 kW connected load for a residential building along with grid supply It is siHgn=iHfieciagnhtt toof ctluarrbifiyneat(mth)is time that the entire simulation analysis carried out in this work rVe=pWreisnedntsspteheedre(mal-/tsi)me hourly power and energy variation in terms of simulation seconds for the software’s convenience. Pm−pu= mechanical power (turbine power) in p.u for the particular values of ρ and A This model is designed to give a maximum power output of 2 kW at 24 m/s cut out speed and it appears at the 11th-time unit and 0 kW at 0 m/s wind velocity is adopted and shown five times in the simulation to study the momentary no wind speed effect on total wind energy generation for 24-time units simulation. This model is designed to give maximum output power of 2 kW at 24 m/s cut out speed
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